The local context model of [1]} ganea2017deep mainly captures the topic level entity relatedness information based on a long range bag-of-words context. To capture latent entity type information, we design a BERT-based entity similarity score \(\Psi _{\rm BERT}(e,c)\) . Specifically, given a short range (the immediate context where the mention \({\rm m}\)  lies) context \(c = ({\rm lctx}, {\rm m}, {\rm rctx})\) , we firstly encode \(c\)  using the same method defined by Equation REF .
\(\mathbf {c} = {\rm BERT}(\lbrace {\rm lctx}, {\rm [MASK]}, {\rm rctx}\rbrace ) \) 
